Initial commit

crates/models/rtx-multimodal/src/fusion/transformer.rs

@@ -0,0 +1,63 @@
+//! Multimodal fusion transformer implementation.
+
+use crate::Result;
+use rtx_tensor::{Device, Tensor};
+use rtx_transformers::architectures::TransformerConfig;
+use rtx_transformers::layers::Layer;
+use rtx_transformers::prelude::{LayerNorm, TransformerBlock};
+
+use super::config::MultimodalFusionConfig;
+
+pub struct MultimodalFusionTransformer {
+    blocks: Vec<TransformerBlock>,
+    ln_f: LayerNorm,
+    config: MultimodalFusionConfig,
+    device: Device,
+}
+
+impl MultimodalFusionTransformer {
+    pub fn new(config: &MultimodalFusionConfig, device: &Device) -> Result<Self> {
+        let transformer_config = TransformerConfig {
+            vocab_size: 32000, // Default vocab size
+            d_model: config.embed_dim,
+            num_layers: 1, // We'll create the layers manually
+            num_heads: config.num_heads,
+            num_key_value_heads: Some(config.num_heads), // Same as num_heads for MHA
+            use_mqa: false,                              // Multi-head attention, not MQA
+            d_ff: config.embed_dim * 4,                  // Standard 4x expansion for FFN
+            max_seq_len: 4096,                           // Default max sequence length
+            dropout: config.dropout as f64,
+            layer_norm_eps: 1e-5,
+            bias: true,
+            activation: "gelu".to_string(),
+        };
+
+        let mut blocks = Vec::new();
+        for _ in 0..config.num_layers {
+            blocks.push(TransformerBlock::new(transformer_config.clone(), device)?);
+        }
+
+        let ln_f = LayerNorm::new(config.embed_dim, 1e-5, true, device)?;
+
+        Ok(Self {
+            blocks,
+            ln_f,
+            config: config.clone(),
+            device: device.clone(),
+        })
+    }
+
+    pub fn forward(&self, vision: &Tensor, audio: &Tensor, text: &Tensor) -> Result<Tensor> {
+        // Concatenate all modalities along sequence dimension
+        let fused = Tensor::cat(&[vision.clone(), audio.clone(), text.clone()], 1)?;
+
+        // Pass through transformer blocks (simplified implementation)
+        let x = fused;
+        // TODO: Implement proper transformer blocks when they have forward methods
+
+        // Final layer norm
+        let x = self.ln_f.forward(&x)?;
+
+        Ok(x)
+    }
+}